Dorian Garrick • Emerging Technologies Commi5ee Breakout 06/14/13 BIF 2013 Emerging Technology Commi5ee Genomics Update on genomic projects and incorporaCon of marker informaCon into geneCc analysis Dorian Garrick [email protected] Iowa State University & NaConal Beef Ca5le EvaluaCon ConsorCum Ge no mics |jēˈnōmiks, -‐ˈnäm-‐, | plural noun [ treated as sing. ] the branch of molecular biology concerned with the structure, funcCon, evoluCon, and mapping of genomes. ORIGIN 1980s: from genome ‘the complete set of genes present in an organism’ + -‐ics Genomic PredicCon for NaConal Ca5le EvaluaCon Traits NBCEC Mission •  OperaConal •  Develop and implement improved predicCons so selecCon can enhance economic viability of US beef ca5le producers – 
– 
– 
– 
American Hereford AssociaCon American Simmental AssociaCon North American Limousin FoundaCon Red Angus AssociaCon of America •  Near OperaConal –  American Gelbvieh AssociaCon –  Maine-‐Anjou AssociaCon of America •  Building Training PopulaCon –  American Brangus Breeders AssociaCon –  American InternaConal Charolais AssociaCon •  Note Canadian contribuCons from Genome Canada www.nbcec.org “Current” 50k Sample Numbers 450 5,557 1,794 5,240 1,418 11,334 3,275 1,440 934 948 32,392 BSH – Shorthorn HER – Hereford RAN – Red Angus SIM – Simmental BRG – Brangus AAN – Aberdeen Angus LIM – Limousin GVH – Gelbvieh CHA – Charolais RDP – Maine Anjou Total 50k Samples Excludes 700k, LD, GGP-‐LD, GGP-‐HD, Bos indicus Change in AHA PredicCve Accuracy 2010 (800) 2012 (1,081) 2013 (2,980) Birth weight 0.40 0.52 0.68 Weaning wt 0.34 0.38 0.52 Yearling wt 0.33 0.44 0.60 Milk 0.21 0.26 0.37 Calving Ease D 0.42 0.68 Calving Ease M 0.20 0.51 Trait Gene$c Correla$ons Fat 0.43 0.44 0.48 Marbling 0.29 0.27 0.43 Ribeye Area 0.41 0.45 0.49 Scrotal Circum 0.25 0.27 0.43 0.33 (11%) 0.37 (13%) 0.52 (27%) Mature Cow wt Average (% gVar) 0.64 Combined PanAmerican International Evaluation
Saatchi et al., JAS 2013; AAABG 2013 1 Dorian Garrick • Emerging Technologies Commi5ee Breakout 06/14/13 (50k) PredicCve AbiliCes Impact on Accuracy-‐-‐%GV=40% Blended Accuracy GeneCc correlaCon=0.64 Acknowledgement Ma@ Spangler Simmental from Single Breed 0.67 Simmental from Pooled Breeds 0.73 Calving ease direct 0.46 0.49 Calving ease maternal 0.31 0.29 Carcass weight 0.61 0.75 Docility 0.10 0.18 Fat thickness 0.19 0.26 Marbling 0.60 0.69 Rib eye muscle area 0.55 0.72 Shear force 0.52 0.60 Stayability 0.51 0.51 Weaning weight direct 0.56 0.63 Weaning weight maternal 0.32 0.28 Yield grade 0.73 0.91 Yearling weight 0.45 0.67 Hereford (2,980) Simmental Limousin (2,800) (2,400) Gelbvieh (1,181) BirthWt 0.75 0.64 0.68 0.65 0.58 0.41 WeanWt 0.67 0.67 0.52 0.52 0.58 0.34 YlgWt 0.69 0.75 0.60 0.45 0.76 Milk 0.51 0.51 0.37 0.34 0.46 0.34 Fat 0.90 0.70 0.48 0.29 REA 0.75 0.75 0.49 0.59 0.63 0.48 Marbling 0.85 0.80 0.43 0.63 0.65 0.56 CED 0.60 0.69 0.68 0.45 0.52 0.48 CEM 0.32 0.73 0.51 0.32 0.51 0.71 0.43 0.45 0.50 GeneCc correlaCons from k-‐fold validaCon (training populaCon size) Saatchi et al (GSE, 2011; 2012; J Anim Sc, 2013) PredicCon of Shorthorn only from other Breeds Pooling Breeds Birth weight RedAngus Angus (6,412) (3,500) SC BIF Accuracy Trait Trait Pooling breeds does not typically hurt predicCons (excepCon is for LIM) Table 1: Across breed genomic predictions for Beef Shorthorn cattle
Angus Brangus Gelbvieh Hereford Limousin
0.08
-0.05
0.09
0.23
0.18
0.05
-0.01
-0.16
0.17
0.15
0.09
0.00
0.08
0.15
0.20
0.05
0.07
-0.10
0.17
0.02
0.11
0.09
-0.04
0.16
-0.06
0.02
0.03
-0.04
0.11
-0.07
-0.08
0.03
0.01
0.12
-0.07
-0.01
0.12
-0.10
0.07
0.15
-0.02
0.09
0.00
-0.08
0.14
0.02
Birth Weight
Calving ease direct
Calving ease maternal
Carcass Weight
Fat tickness
Milk
Marbling
Rib eye area
Weaning weight
Yearling weight
Red Angus
0.40
0.23
0.06
0.23
0.08
0.03
0.09
0.05
0.15
0.13
Simmental
0.37
0.30
0.07
0.20
0.01
-0.06
0.17
0.08
0.09
0.13
Across breed predicCon does not work if the breed is not in training See also Kachman et al., 2013 GSE Saatchi & Garrick, WSASAS 2013 Illumina Bovine 770k, 50k (v2), 3k Diﬀerent Illumina BeadChips •  Illumina •  GeneSeek –  50k $80 –  GeneSeek Genomic Proﬁlers •  Several versions –  700k (HD) $185 –  3k (LD) $45 •  Low Density 1
–  Super GGP (20k) $45 •  High Density –  GGP HD (77k) $75 –  GGP HD (Indicus) 2 Dorian Garrick • Emerging Technologies Commi5ee Breakout 06/14/13 Panel Comparison Orange = GGP-‐Super LD 19k Green = GGP-‐HD (taurus) 70k Black = Illumina 50K Blue = Illumina HD 770K ImputaCon •  Is a method of determining some genotypes on a computer using actual genotypes on relaCves •  Provides opCons for cheaper rouCne genotyping •  Requires relaCves to have been genotyped at higher density –  Will beneﬁt from a larger training populaCon –  Will beneﬁt from relaCves having been genotyped GGP also include custom SNP 50k and GGP-‐HD share 28K 50k and GGP-‐Super LD share 8k •  Requires knowledge of the order of markers Also a separate GGP-‐HD-‐I (Indicus) Imputation from Mixed Density Genotypes Imputation from Mixed Density Genotypes Sire
paternal
maternal
Sire
paternal
maternal
Progeny
paternal
maternal
Progeny
paternal
maternal
Dam
paternal
maternal
Dam
paternal
maternal
Imputation from Mixed Density Genotypes Sire
paternal
maternal
Progeny
paternal
maternal
Dam
paternal
maternal
AHA PredicCve Accuracy 2,980 6-‐fold Actual = 50k Imputed = 10k 3 Dorian Garrick • Emerging Technologies Commi5ee Breakout 06/14/13 Major Regions for Birth Weight QTL CharacterizaCon Genetic Variance %
Chr_mb Angus Hereford Shorthorn Limousin Simmental Gelbvieh AHA 2,980 Genome bw ww yw mcw ced 7_93 7.10 5.85 0.01 0.02 0.18 0.02 bw 1.00 6_38-‐39 0.47 8.48 11.63 5.90 16.3 4.75 ww 0.44 1.00 20_4 3.70 7.99 1.19 0.07 1.53 0.03 yw 0.46 0.68 14_24-‐26 0.42 0.01 0.01 0.71 3.05 8.14 mcw 0.44 0.53 0.58 1.00 ced -‐0.51 -‐0.12 -‐0.16 -‐0.28 1.00 6_38 0.19 0.05 0.08 0.13 0.30 7_93 0.04 0.02 0.02 0.02 0.02 20_4 0.08 0.05 0.07 0.17 0.09 Some eﬀects appear to be missing in some breeds Some breeds may be nearly all homozygous for the large or small variant on chromosomes 9 & 14? Some of these same regions have big eﬀects on one or more of weaning weight, yearling weight, marbling, ribeye area, calving ease Common haplotypes at BW QTL 20 SNP can have >1m haplotypes Window Genome shows geneCc correlaCons, Window shows proporCon geneCc variance 7_93 (n>9) in Diﬀerent Breeds /Users/dorian/Desktop/untitled folder/tmp
Saved: 4/17/13 8:54:16 PM
Haplotypes from Beagle 1
2
4/4 7_93 (11 SNP) 1075 01101110111 253 00101110111 181 11101110111 71 11101110101 58 01001110101 33 00100110101 29 01000110101 27 11001110101 24 11111110111 22 11001110111 20 01100110111 19 00101110100 10 01101110100 6_38 (23 SNP) 216 10010000000111000010111 132 10111100010101011000110 121 10011101011111010010101 118 10110100010111011010111 113 00110101001101100010111 95 10111100011101011000100 80 00011101111011010010101 74 11111101010110011101100 68 10111101010101011000110 67 10010001000111000010111 63 11111100010110011101100 53 10111101011101011000100 52 1 1111101110010001111100 Note there are no tag SNPs! 20_4 (28 SNP) 761 0100110011011100001101011111 136 0110100110110010011001110111 124 0110010101000111011011110011 107 1110110011000000010010100011 88 1100011011001000010101111111 52 0110111111010111000011110001 50 0101110101111001010011111010 49 1111010011001110011011000001 49 0100110110101110000010100010 31 0100110111001010001101011111 26 0110000101000000011011110011 22 0100100111001010001011000010 20 1100011011011000010101111111 Among 941 Herefords SegregaCon Status •  Determining the –  frequency of the alternate QTL alleles –  QTL genotype of individual animals 1.00 3
4
5
6
7
8
3/4 9
10
11
12
13
14
15
16
2/4 17
18
19
20
21
1/4 22
1655_AAN 1655_HER
1780_AAN 1780_HER
1911_AAN 1911_HER
311_AAN 311_HER
375_AAN 375_HER
887_AAN 887_HER
372_AAN 372_HER
1591_AAN
1716_AAN
1653_HER
1909_HER
823_HER
116_AAN
565_HER
629_HER
1655_LIM
1780_LIM
1911_LIM
311_LIM
375_LIM
887_LIM
1591_LIM
1716_LIM
1653_LIM
1909_LIM
823_LIM
116_LIM
565_LIM
629_LIM
1652_LIM
1844_LIM
1845_LIM
1847_LIM
1908_LIM
885_LIM
1_pteAAN 2_pteHER 11_pteLIM
1655_SIM
1780_SIM
1911_SIM
311_SIM
375_SIM
887_SIM
372_SIM
1591_SIM
1716_SIM
1653_SIM
1909_SIM
823_SIM
1652_SIM
1844_SIM
1845_SIM
1847_SIM
1908_SIM
885_SIM
8_pteSIM
Page 1 of 1
Printed For: Dorian Garrick
Some haplotypes shared across breeds Many private haplotypes so across breed predicCon needs all breeds in training MulCple Trait Genomic PredicCon •  PracCcally all genomic predicCon analyses that allow SNP markers to have diﬀerent weights, do this on a single trait basis •  We have prototyped an approach that allows mulCple trait genomic predicCon with diﬀerent markers have diﬀerent weights 4 Dorian Garrick • Emerging Technologies Commi5ee Breakout 06/14/13 Combined Analysis of Genotyped & Non-‐Genotyped Individuals Henderson’s Mixed Model EquaCons are the basis for NaConal Ca5le EvaluaCon !!!!! !
!!!!! !
!!!!! !
!!!!! !
!
!
!!!!! ! ! !!! !
!!!!! !
TradiConally based on inverse relaConship matrix One approach to a “Single-‐step” analysis modiﬁes the inverse of the pedigree-‐based relaConship matrix according to a genomic relaConship matrix H (below) is used in place of G (above) Summary •  Genomic predicCon is an immature technology –  Its accuracy exceeds parent average EPD –  Accuracy is conCnuously improving •  AdopCon is rapidly acceleraCng •  Currently used methodologies will soon be superseded with alternaCve approaches Inverse pedigree relaConship matrix Inverse genomic relaConship matrix Aguilar et al. (2010) Acknowledgements University of Missouri Dr. Jerry Taylor Dr. Robert Schnabel Dr. Jared Decker GeneSeek Dr. Stewart Bauck Dr. Elisa Marques CalPoly San Luis Obispo Dr. Bruce Golden Dr. Chris Lupo Iowa State University Dr. Mahdi Saatchi Dr. Rohan Fernando Dr. Kadir Kizilkaya Dr. Jim Reecy Dr. James Koltes Xiaochen Sun Jian Zeng Ziqing Weng University of Nebraska Dr. Ma5 Spangler Dr. Stephen Kachman USDA – US-‐MARC Dr. John Pollak University of Kentucky Dr. Darrh Bullock Kansas State University Dr. Bob Weaber UC -‐ Davis Dr. Alison Van Eenennaam Colorado State University Dr. Milt Thomas ParCcipaCng Breed AssociaCons 5